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Schlesner, M.

Publications and source records attributed to Schlesner, M..

At least 19 recordsLinked to original sources

Multi-omic dissection of reversible and persistent molecular alterations in diet-induced obesity

Diet-induced obesity drives broad molecular remodeling across host and microbial systems, but why lifestyle intervention reverses some of these alterations while others persist remains unclear. To address this, we performed a multi-omic characterization of diet-induced obese mice after a combined nutritional and exercise intervention, integrating liver transcriptomics, epigenomics, metabolomics and metallomics with gut metagenomics and metallomics. Multi-omics factor analysis resolved two dominant axes of variation. The first captured a broadly reversible response (~72% of altered variables), restored by dietary restriction and exercise, involving coordinated remodeling of hepatic metal homeostasis, epigenetic regulation, and immune and cell-turnover pathways. The second comprised persistent alterations resistant to intervention and driven primarily by microbial functional profiles: notably, functional diversity remained reduced despite substantial taxonomic recovery. Using germ-free mice to define the microbiota-responsive hepatic space, we found that a significant fraction of the persistent liver features fell within it, enriched in lipid metabolism (PPAR signaling, steroid and cholesterol biosynthesis, peroxisomal activity) and retinol metabolism. Sequential correlation analysis traced these changes to the loss of specific low-abundance taxa and their biosynthetic capacities, particularly vitamin (folate, biotin, cobalamin, pantothenate, thiamine) and cofactor metabolism, implicating microbiota-derived vitamin metabolism in sustained hepatic dysfunction. Cobalt was the sole essential element that remained persistently dysregulated and tracked dietary cobalt content, suggesting a diet--microbiota route to the persistent phenotype. These findings establish a dual regulatory framework in which metabolic plasticity is governed by reversible host-intrinsic and persistent microbiota-dependent processes, providing a systems-level explanation for obesogenic memory.

systems biology↗

Pipeline Olympics: continuable benchmarking of computational workflows for DNA methylation sequencing data against an experimental gold-standard

DNA methylation is a widely studied epigenetic mark and a powerful biomarker of cell type, age, environmental exposures, and disease. Whole-genome sequencing following selective conversion of unmethylated cytosines into thymines via bisulfite treatment or enzymatic methods remains the reference method for DNA methylation profiling genome-wide. While numerous software tools facilitate processing of DNA methylation sequencing reads, a comprehensive benchmarking study has been lacking thus far. In this study, we systematically compared complete computational workflows for processing DNA methylation sequencing data using a dedicated benchmarking dataset generated with five genome-wide profiling protocols. As an evaluation reference, we employed highly quantitative locus-specific measurements from our preceding benchmark of targeted DNA methylation assays. Based on this experimental gold-standard assessment and several comprehensive metrics, we identified workflows that consistently demonstrated superior performance and revealed major workflow development trends. To facilitate the sustainability of our benchmark, we implemented an interactive workflow execution and data presentation platform, adaptable to user-defined criteria and seamlessly expandable to future software.

bioinformatics↗

Pyjacker identifies enhancer hijacking events in acute myeloid leukemia including MNX1 activation via deletion 7q

Acute myeloid leukemia with complex karyotype (ckAML) is characterized by high genomic complexity, including frequent TP53 mutations and chromothripsis. We hypothesized that the numerous genomic rearrangements could reposition active enhancers near proto-oncogenes, leading to their aberrant expression. We developed pyjacker, a computational tool for the detection of enhancer hijacking events, and applied it to a cohort of 39 ckAML samples. Pyjacker identified motor neuron and pancreas homeobox 1 (MNX1), a gene aberrantly expressed in 1.4% of AML patients, often as a result of del(7)(q22q36) associated with hijacking of a CDK6 enhancer. MNX1-activated cases show significant co-occurrence with BCOR mutations and a gene signature shared with t(7;12)(q36;p13) pediatric AML. We demonstrated that MNX1 is a dependency gene, as its knockdown in a xenograft model reduces leukemia cell fitness. In conclusion, enhancer hijacking is a frequent mechanism for oncogene activation in AML. Statement of significanceThis study examines the consequences of structural alterations and demonstrates that proto-oncogene activation by enhancer hijacking is an overlooked pathomechanism in AML. MNX1 overexpression demonstrates that deletions on chromosome 7q can not only lead to haploinsufficiency, but also to activation of oncogenes by enhancer hijacking, providing a novel leukemogenic mechanism.

cancer biology↗

Oncogenic RAS-Pathway Activation Drives Oncofetal Reprogramming and Creates Therapeutic Vulnerabilities in Juvenile Myelomonocytic Leukemia

Aberrant fetal gene expression facilitates tumor-specific cellular plasticity by hijacking molecular programs of embryogenesis1. Persistent fetal gene signatures in childhood malignancies are typically explained by their prenatal origins2-6. In contrast, reactivation of fetal gene expression is considered a consequence of oncofetal reprogramming (OFR) in adult malignancies and is associated with aggressive disease7-10. To date, OFR has not been described in the context of childhood malignancies. Here, we performed a comprehensive multi-layered molecular characterization of juvenile myelomonocytic leukemia (JMML) and identified OFR as a hallmark of aggressive JMML. We observed that hematopoietic stem cells (HSCs) aberrantly express mixed developmental programs in JMML. Expression of fetal gene signatures combined with a postnatal epigenetic landscape suggested OFR, which was validated in a JMML mouse model, demonstrating that postnatal activation of RAS signaling is sufficient to induce fetal gene signatures. Integrative analysis identified the fetal HSC maturation marker CD52 as a novel therapeutic target for aggressive JMML. Anti-CD52 treatment depleted human JMML HSCs and disrupted disease propagation in vivo. In summary, this study implicates OFR, defined as postnatal acquisition of fetal transcription signatures, in the pathobiology of a childhood malignancy. We provide evidence for the direct involvement of oncogenic RAS signaling in OFR. Finally, we demonstrate how OFR can be leveraged for the development of novel treatment strategies. Highlights{blacksquare} Epigenomic and transcriptomic landscape of juvenile myelomonocytic leukemia (JMML) in the context of hematopoietic development. {blacksquare}The presence of fetal transcription signatures in childhood malignancies is not indicative of a developmental maturation block. {blacksquare}High-risk JMML is characterized by oncofetal reprogramming of postnatal hematopoietic stem cells (HSCs). {blacksquare}RAS-pathway mutations induce fetal-like gene expression signatures in murine postnatal HSCs. {blacksquare}The fetal maturation marker CD52 is a novel therapeutic target in high-risk JMML.

cancer biology↗

RBM39 shapes innate immunity through transcriptional and splicing control of IRF3 and other key factors

RNA-binding motif protein 39 (RBM39) is an RNA-binding protein involved in tumorigenesis, cell metabolism, and development. Here, we performed a genome-wide CRISPR/Cas9 screen in two liver-derived cell lines and identified RBM39 as a regulator of cell intrinsic innate immune responses. The knockdown of RBM39 or the treatment with Indisulam, an aryl sulfonamide drug targeting RBM39 for proteasomal degradation, strongly reduced the induction of interferon-stimulated genes (ISGs) in response to double-stranded RNA (dsRNA) or viral infections upon sensing by toll-like receptor 3 (TLR3) or cytosolic RIG-I-like receptors. RNA sequencing (seq) and mass spectrometry identified that transcription and/or splicing of the key pathway components IRF3, RIG-I, and MDA5 were affected by RBM39 depletion. RBM39 knockdown further restrained type I and type III IFN pathways, by reducing expression of the type I IFN receptor subunit interferon alpha and beta receptor subunit 2 (IFNAR2), type III IFN receptor subunit interleukin 10 receptor subunit beta (IL-10RB) and transcription factor signal transducer and activator of transcription (STAT) 1 and 2. RBM39 overall orchestrates innate immunity by regulating basal expression of key factors of the interferon response via transcription and/or alternative splicing. SignificanceThe function of RBM39 in tumorigenesis has been investigated intensively in the last decade, but its immunological role is still largely unknown. In our study, we identified RBM39 as a regulatory factor of cell intrinsic signaling via a CRISPR/Cas9 screen. Depletion of RBM39 impairs TLR3, RIG-I/MDA5, and IFN pathways, and thus attenuates innate immune responses. Our omics analysis revealed that RBM39 governs the basal expression of several key factors within these pathways, such as RNA sensors RIG-I and MDA5, type I/III receptors, transcription factors IRF3, STAT1 and STAT2, via its transcriptional and splicing function. Therefore, RBM39 might be a therapeutic target to modulate innate immunity, e.g. in the context of autoimmune disorders.

immunology↗

Endothelial Dnmt3a controls placenta vascularization and function to support fetal growth

The fetoplacental capillary network is of vital importance for proper nourishment during early development. Inadequate maternal-fetal circulation has emerged as one of the main pathophysiological features of placental insufficiency. Meta-analysis of human placental endothelial cells (EC) revealed that downregulation of the de novo DNA methyltransferase 3A (DNMT3A) is associated with preeclampsia. However, mechanistic insights into functional consequences of altered DNA methylation patterns during placental vascular development remain elusive. Here, we investigated the role of Dnmt3a in the vasculature during murine placenta development. Spatial and temporal expression analyses revealed an induction of Dnmt3a in the mature labyrinth layer. The global and endothelium-specific loss (ECKO) of Dnmt3a resulted in reduced placental vascularization and fetal growth restriction. EC deleted for Dnmt3a demonstrated extensive loss of DNA methylation, particularly close to angiogenesis related genes. Loss of DNA methylation decreased the angiogenic capacity of EC in vitro and in vivo. Collectively, these data identify DNMT3A as the main DNA methyltransferase in the human and murine placental vasculature and characterize its importance for physiological endothelial function. The Dnmt3a-dependent regulation of genes related to placenta insufficiency validates Dnmt3a ECKO mice as an epigenetically driven mouse model of placenta insufficiency with potential translational relevance.

developmental biology↗

Dynamic DNA methylation reveals novel cis-regulatory elements in murine hematopoiesis

BackgroundThe differentiation of hematopoietic stem and progenitor cells (HSPCs) to terminally differentiated immune cells is accompanied by large-scale remodeling of the DNA methylation landscape. While significant insights into the molecular mechanisms of hematopoietic tissue regeneration were derived from mouse models, profiling of DNA methylation changes has been hampered by high cost or low resolution using the methods available. This problem has been overcome by the recent development of the Infinium Mouse Methylation BeadChip (MMBC) array, facilitating methylation profiling of the mouse genome at single CpG resolution at affordable cost. ResultsWe extended the RnBeads package to provide a computational framework for the analysis of MMBC data. This framework was applied to a newly generated MMBC reference map of mouse hematopoiesis encompassing nine different cell types. The analysis of dynamically regulated CpG sites showed progressive and unidirectional DNA methylation changes from HSPCs to differentiated hematopoietic cells and allowed the identification of lineage- and cell type-specific DNA methylation programs. Comparison to previously published catalogues of cis-regulatory elements (CREs) revealed 12,856 novel putative CREs which were dynamically regulated by DNA methylation (mdCREs). These mdCREs were predominantly associated with patterns of cell type-specific DNA hypomethylation and could be identified as epigenetic control regions regulating the expression of key hematopoietic genes during differentiation. ConclusionsWe established a publicly available analysis pipeline for MMBC datasets and provide a DNA methylation atlas of mouse hematopoiesis. This resource allowed us to identify novel putative CREs involved in hematopoiesis and will serve as a platform to study epigenetic regulation of normal and malignant hematopoiesis.

genomics↗

A connectivity signature for glioblastoma

Tumor cell extensions called tumor microtubes (TMs) in glioma resemble neurites during neurodevelopment and connect glioma cells to a network that has considerable relevance for tumor progression and therapy resistance. The determination of interconnectivity in individual tumors has been challenging and the impact of tumor cell connectivity on patient survival remained unresolved so far. Here, a connectivity signature from single-cell RNA-sequenced (scRNA-Seq) xenografted primary glioblastoma (GB) cells was established and clinically validated. Thirty-four of 40 connectivity genes were related to neurogenesis, neural tube development or glioma progression, including the TM-network-relevant GAP43 gene. Astrocytic-like and mesenchymal-like GB cells had the highest connectivity signature scores in scRNA-Seq data of patient-derived xenografts and patient samples. In 230 human GBs, high connectivity correlated with the mesenchymal expression subtype, TP53 wildtype, and with dismal patient survival. CHI3L1 was identified as a robust molecular marker of connectivity. Thus, the connectivity signature allows novel insights into brain tumor biology, provides a proof-of-principle that tumor cell connectivity is relevant for patients prognosis, and serves as a robust biomarker that can be used for future clinical trials. Statement of significanceIntegration of GB cells into functional networks drives tumor progression and resistance. Here, we established and validated a novel connectivity gene expression signature of single GB cells and whole tumors that can be easily applied to clinical and preclinical samples. It is shown that connectivity is determining prognosis combining molecular, functional and clinical insights into the disease.

cancer biology↗

Deep molecular characterization linked to drug response profiling of pancreatic ductal adenocarcinoma using patient-derived organoids.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by high drug resistance and poor prognosis. Novel therapeutic and stratification strategies are urgently needed. Here, we present an integration of in-depth genomic and transcriptomic characterization with drug screening and clinical outcome based on a catalogue of 51 patient-derived tumor organoids (PDOs) from resected PDAC. Known PDAC molecular subtypes and their prognostic value are conserved in organoids. Integration of transcriptomic and drug response profiles suggest a metabolism-mediated modulations of drug resistance. Copy number alterations on chromosome 13q and wild-type status of TP53 emerged as potential novel genomic biomarkers for sensitivity to 5-FU and oxaliplatin treatment, respectively. Functional testing of targeted drugs in PDOs revealed its additional value for genome-driven personalized oncology. Co-deletion of TP53/POLR2A increased vulnerability to RNA polymerase II inhibition, pointing to a promising target for personalized treatment in PDAC. SignificancePatient-derived PDAC organoids hold great promise as surrogate tumor models for personalized oncology. By integrating highly granular molecular, drug sensitivity and clinical data, we demonstrate that PDOs are valid models for molecular characterization and response prediction that also enable identification of novel drug sensitivity biomarkers and resistance mechanisms in PDAC.

cancer biology↗

Reprogramming of stromal fibroblasts by chemotherapy-induced secretion of IFNβ1 drives re-growth of breast cancer cells after treatment.

Chemotherapy is still the standard of care for a large number of aggressive tumours including breast cancer. In breast cancer, chemotherapeutic regimens are administered in intervaled cycles of the maximum tolerated dose, allowing cancer cells to re-grow or adapt during the resting periods between cycles. However, how stromal fibroblasts impact the fate of cancer cells after chemotherapy treatment remains poorly understood. We show that cancer cells utilize paracrine signalling with stromal fibroblasts to drive their recovery after treatment withdrawal. Secretion of IFN{beta}1 by cancer cells after treatment with high doses of chemotherapy instigates the acquisition of an anti-viral state in stromal fibroblasts associated with the expression of several interferon stimulated genes (ISGs), including numerous pro-inflammatory cytokines. This crosstalk is an important driver of the expansion of breast cancer cells after chemotherapy and blocking of IFN{beta}1 in tumour cells abrogated their increased recovery potential. Analysis of human breast carcinomas supports the proposed role of IFN{beta}1 since its expression is inversely correlated with recurrence free survival (RFS). Moreover, expression of the interferon signature identified in stromal fibroblasts is equally associated with higher recurrence rates and a worse outcome in breast cancer patients. Our study unravels a novel paracrine communication between cancer cells and fibroblasts that ultimately results in the escape of malignant cells to treatment. Targeting of this axis could potentially improve the outcome of breast cancer patients to chemotherapy treatment.

cancer biology↗

Dissecting intratumor heterogeneity of nodal B cell lymphomas on the transcriptional, genetic, and drug response level

Tumor heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is well-known to affect the efficacy of anti-cancer drugs, most personalized treatment approaches do not account for intratumor heterogeneity. We addressed this issue by studying the heterogeneity of lymph node-derived B cell non-Hodgkin lymphoma (B-NHL) by single cell RNA-sequencing (scRNA-seq) and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subclones and compared their drug response and genomic profiles. Malignant subclones of the same patient responded strikingly different to anti-cancer drugs ex vivo, which recapitulated subclone-specific drug sensitivity during in vivo treatment. Tumor infiltrating T cells represented the majority of non-malignant cells, whose gene expression signatures were similar across all donors, whereas the frequencies of T cell subsets varied significantly between the donors. Our data provide new insights into the heterogeneity of B-NHL and highlight the relevance of intratumor heterogeneity for personalized cancer therapies.

cancer biology↗

Segmentation-free inference of cell-types from in situ transcriptomics data

Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a novel method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. We found that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

bioinformatics↗

HTSlib-js: Developer toolkit for a client-side JavaScript interface for HTSlib

BackgroundAn increasing number of bioinformatics tools are developed in JavaScript to provide an interactive visual interface to users. However, there are no tools yet that enable fast client-side analysis of large, high-throughput sequencing file formats, such as BAM and VCF files.\n\nResultsWe present HTSlib-js, an asm.js-based JavaScript wrapper for HTSlib, the de facto standard for processing BAM and VCF files. HTSlib-js exploits recent technological advances in web browser engines that dramatically increase the performance of browser-based tools to enable swift processing of files in the aforementioned formats. HTSlib-js enables quick development of JavaScript-based applications that include processing of aligned sequence reads and variant calling data.\n\nConclusionsHTSlib-js constitutes a toolkit for developers to easily write fast, accessible, browser-based applications that place an emphasis on data visualization, interactivity and privacy. Real-world examples demonstrate the capabilities of HTSlib-js and serve as guide for own developments.

bioinformatics↗

Portraits of genetic intra-tumour heterogeneity and subclonal selection across cancer types

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin and drivers of ITH across cancer types are poorly understood. To address this question, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples, spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions, with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types, and identify cancer type specific subclonal patterns of driver gene mutations, fusions, structural variants and copy-number alterations, as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution, and provide an unprecedented pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

cancer biology↗

pheno-seq - linking 3D phenotypes of clonal tumor spheroids to gene expression

3D-culture systems have advanced cancer modeling by reflecting physiological characteristics of in-vivo tissues, but our understanding of functional intratumor heterogeneity including visual phenotypes and underlying gene expression is still limited. Single-cell RNA-sequencing is the method of choice to dissect transcriptional tumor cell heterogeneity in an unbiased way, but this approach is limited in correlating gene expression with contextual cellular phenotypes.\n\nTo link morphological features and gene expression in 3D-culture systems, we present pheno-seq for integrated high-throughput imaging and transcriptomic profiling of clonal tumor spheroids. Specifically, we identify characteristic EMT expression signatures that are associated with invasive growth behavior in a 3D breast cancer model. Additionally, pheno-seq determined transcriptional programs containing lineage-specific markers that can be linked to heterogeneous proliferative capacity in a patient-derived 3D model of colorectal cancer. Finally, we provide evidence that pheno-seq identifies morphology-specific genes that are missed by scRNA-seq and inferred single-cell regulatory states without acquiring additional single cell expression profiles. We anticipate that directly linking molecular features with patho-phenotypes of cancer cells will improve the understanding of intratumor heterogeneity and consequently be useful for translational research.

genomics↗

Identification and prioritisation of causal variants in human genetic disorders from exome or whole genome sequencing data

With genome sequencing entering the clinics as diagnostic tool to study genetic disorders, there is an increasing need for bioinformatics solutions that enable precise causal variant identification in a timely manner.\n\nBackgroundWorkflows for the identification of candidate disease-causing variants perform usually the following tasks: i) identification of variants; ii) filtering of variants to remove polymorphisms and technical artifacts; and iii) prioritization of the remaining variants to provide a small set of candidates for further analysis.\n\nMethodsHere, we present a pipeline designed to identify variants and prioritize the variants and genes from trio sequencing or pedigree-based sequencing data into different tiers.\n\nResultsWe show how this pipeline was applied in a study of patients with neurodevelopmental disorders of unknown cause, where it helped to identify the causal variants in more than 35% of the cases.\n\nConclusionsClassification and prioritization of variants into different tiers helps to select a small set of variants for downstream analysis.

bioinformatics↗

ACEseq - allele specific copy number estimation from whole genome sequencing

ACEseq is a computational tool for allele-specific copy number estimation in tumor genomes based on whole genome sequencing. In contrast to other tools it features GC-bias correction, unique replication timing-bias correction and integration of structural variant (SV) breakpoints for improved genome segmentation. ACEseq clearly outperforms widely used state-of-the art methods, provides a fully automated estimation of tumor cell content and ploidy, and additionally computes homologous recombination deficiency scores.

bioinformatics↗

Deciphering programs of transcriptional regulation by combined deconvolution of multiple omics layers

Metazoans are crucially dependent on multiple layers of gene regulatory mechanisms which allow them to control gene expression across developmental stages, tissues and cell types. Multiple recent research consortia have aimed to generate comprehensive datasets to profile the activity of these cell type- and condition-specific regulatory landscapes across many different cell lines and primary cells. However, extraction of genes or regulatory elements specific to certain entities from these datasets remains challenging. We here propose a novel method based on non-negative matrix factorization for disentangling and associating huge multi-assay datasets including chromatin accessibility and gene expression data. Taking advantage of implementations of NMF algorithms in the GPU CUDA environment full datasets composed of tens of thousands of genes as well as hundreds of samples can be processed without the need for prior feature selection to reduce the input size. Applying this framework to multiple layers of genomic data derived from human blood cells we unravel mechanisms of regulation of cell type-specific expression in T-cells and monocytes.

genomics↗